Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Lahiri, Shuvendu K"'
Autor:
Chen, Tianyu, Lu, Shuai, Lu, Shan, Gong, Yeyun, Yang, Chenyuan, Li, Xuheng, Misu, Md Rakib Hossain, Yu, Hao, Duan, Nan, Cheng, Peng, Yang, Fan, Lahiri, Shuvendu K, Xie, Tao, Zhou, Lidong
Ensuring correctness is crucial for code generation. Formal verification offers a definitive assurance of correctness, but demands substantial human effort in proof construction and hence raises a pressing need for automation. The primary obstacle li
Externí odkaz:
http://arxiv.org/abs/2410.15756
Autor:
Lahiri, Shuvendu K.
Publikováno v:
Proceedings of the 24th Conference on Formal Methods in Computer Aided Design (FMCAD 2024)
Verification-aware programming languages such as Dafny and F* provide means to formally specify and prove properties of a program. Although the problem of checking an implementation against a specification can be defined mechanically, there is no alg
Externí odkaz:
http://arxiv.org/abs/2406.09757
Vectorization is a powerful optimization technique that significantly boosts the performance of high performance computing applications operating on large data arrays. Despite decades of research on auto-vectorization, compilers frequently miss oppor
Externí odkaz:
http://arxiv.org/abs/2406.04693
Improper parsing of attacker-controlled input is a leading source of software security vulnerabilities, especially when programmers transcribe informal format descriptions in RFCs into efficient parsing logic in low-level, memory unsafe languages. Se
Externí odkaz:
http://arxiv.org/abs/2404.10362
Publikováno v:
in IEEE Transactions on Software Engineering, vol. 50, no. 09, pp. 2254-2268, 2024
Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent. However, given NL is informal, it does not lend easily to checking that the gene
Externí odkaz:
http://arxiv.org/abs/2404.10100
Autor:
Kamath, Adharsh, Senthilnathan, Aditya, Chakraborty, Saikat, Deligiannis, Pantazis, Lahiri, Shuvendu K., Lal, Akash, Rastogi, Aseem, Roy, Subhajit, Sharma, Rahul
Loop invariants are fundamental to reasoning about programs with loops. They establish properties about a given loop's behavior. When they additionally are inductive, they become useful for the task of formal verification that seeks to establish stro
Externí odkaz:
http://arxiv.org/abs/2311.07948
Autor:
Chakraborty, Saikat, Lahiri, Shuvendu K., Fakhoury, Sarah, Musuvathi, Madanlal, Lal, Akash, Rastogi, Aseem, Senthilnathan, Aditya, Sharma, Rahul, Swamy, Nikhil
Synthesizing inductive loop invariants is fundamental to automating program verification. In this work, we observe that Large Language Models (such as gpt-3.5 or gpt-4) are capable of synthesizing loop invariants for a class of programs in a 0-shot s
Externí odkaz:
http://arxiv.org/abs/2310.09342
Informal natural language that describes code functionality, such as code comments or function documentation, may contain substantial information about a programs intent. However, there is typically no guarantee that a programs implementation and nat
Externí odkaz:
http://arxiv.org/abs/2310.01831
Language models of code (LMs) work well when the surrounding code provides sufficient context. This is not true when it becomes necessary to use types, functionality or APIs defined elsewhere in the repository or a linked library, especially those no
Externí odkaz:
http://arxiv.org/abs/2306.10763